ArticleEcology and evolution2018
Digital photography provides a fast, reliable, and noninvasive method to estimate anthocyanin pigment concentration in reproductive and vegetative plant tissues.
Article in Ecology and evolution, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Remote Sensing of Endogenous Pigmentation by Inducible Synthetic Circuits in Grasses.Plant biotechnology journal · 2026Article
- Temperature and the evolution of flower color: A review.American journal of botany · 2026Review
- Heat drastically alters floral color and pigment composition without affecting flower conspicuousness.American journal of botany · 2026Article
- APTES: a high-throughput deep learning-based Arabidopsis phenotypic trait estimation system for individual leaves and siliques.aBIOTECH · 2025Article
- Green flowers need yellow to get noticed in a green world.Annals of botany · 2025Article
- Machine learning and digital imaging for spatiotemporal monitoring of stress dynamics in the clonal plant Carpobrotus edulis: uncovering a functional mosaic.Annals of botany · 2025Article
- Transcontinental patterns in floral pigment abundance among animal-pollinated species.Scientific reports · 2025Article
- Intrapopulation variability in coloration is associated with reproductive season in the crayfishCurrent zoology · 2025Article
- Coding-Sequence Evolution Does Not Explain Divergence in Petal Anthocyanin Pigmentation Between Mimulus luteus Var luteus and M. l. variegatus.Evolution & development · 2025Article
- Analysis of coat texture characteristics of bread wheat grains obtained from digital images.Frontiers in plant science · 2025Article
- Development of an Automated Low-Cost Multispectral Imaging System to Quantify Canopy Size and Pigmentation.Sensors (Basel, Switzerland) · 2024Article
- Image-based phenotyping to estimate anthocyanin concentrations in lettuce.Frontiers in plant science · 2023Article
- Development of a longevity prediction model for cut roses using hyperspectral imaging and a convolutional neural network.Frontiers in plant science · 2023Article
- Development of an Image Analysis Pipeline to Estimate Sphagnum Colony Density in the Field.Plants (Basel, Switzerland) · 2021Article
- ARADEEPOPSIS, an Automated Workflow for Top-View Plant Phenomics using Semantic Segmentation of Leaf States.The Plant cell · 2020Article
- Sex-specific floral attraction traits in a sequentially hermaphroditic species.Ecology and evolution · 2020Article
- Climate Predicts UV Floral Pattern Size, Anthocyanin Concentration, and Pollen Performance inFrontiers in plant science · 2020Article
- UV radiation increases phenolic compound protection but decreases reproduction in Silene littorea.PloS one · 2020Article
- The appearance of mimetic Heliconius butterflies to predators and conspecifics.Evolution; international journal of organic evolution · 2018Article
- Digital photography provides a fast, reliable, and noninvasive method to estimate anthocyanin pigment concentration in reproductive and vegetative plant tissues.Ecology and evolution · 2018Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Anthocyanin pigments have become a model trait for evolutionary ecology as they often provide adaptive benefits for plants. Anthocyanins have been traditionally quantified biochemically or more recently using spectral reflectance. However, both methods require destructive sampling and can be labor intensive and challenging with small samples. Recent advances in digital photography and image processing make it the method of choice for measuring color in the wild. Here, we use digital images as a quick, noninvasive method to estimate relative anthocyanin concentrations in species exhibiting color variation. Using a consumer-level digital camera and a free image processing toolbox, we extracted RGB values from digital images to generate color indices. We tested petals, stems, pedicels, and calyces of six species, which contain different types of anthocyanin pigments and exhibit different pigmentation patterns. Color indices were assessed by their correlation to biochemically determined anthocyanin concentrations. For comparison, we also calculated color indices from spectral reflectance and tested the correlation with anthocyanin concentration. Indices perform differently depending on the nature of the color variation. For both digital images and spectral reflectance, the most accurate estimates of anthocyanin concentration emerge from anthocyanin content-chroma ratio, anthocyanin content-chroma basic, and strength of green indices. Color indices derived from both digital images and spectral reflectance strongly correlate with biochemically determined anthocyanin concentration; however, the estimates from digital images performed better than spectral reflectance in terms of
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